摘要
间歇过程作为制造业的重要生产方式之一,其高效运行是智能制造的优先主题.为了保障生产过程的高效运行,面向间歇生产的过程数据解析与状态监控算法在最近三十年间得到大家的广泛关注,发展速度稳步提升.但由于间歇过程本身的多重时变大范围非平稳运行复杂特性,以及对状态监控与故障诊断要求的提高,现有的理论和方法仍面临着挑战.本文从分析间歇过程的特性出发,从数据解析的角度,总结了近三十年来非平稳间歇过程高性能监控研究的发展.一方面对间歇过程监控领域几种经典的方法体系进行了总结和梳理,另一方面揭示了尚存在的问题以及未来可能的研究思路和发展脉络.
Batch process is an important class of manufacturing processes.Its condition operation has been given high priority for smart manufacturing,which closely depends on the automatic condition monitoring and fault diagnosis.Great efforts have been made in the research on data analytics and high-efficiency monitoring algorithms with significant development for batch processes during the past thirty years.However,due to its complex characteristics and increasing requirements on monitoring and diagnosis precision,there are still many challenging problems in this field.In this paper,starting from the nature of batch process and data analystics,we address the challenges in this field,review the development of monitoring and diagnosis strategies,analyze several classical algorithms,and discuss the future development of batch process high-efficiency monitoring.
作者
赵春晖
余万科
高福荣
ZHAO Chun-Hui;YU Wan-Ke;GAO Fu-Rong(College of Control Science and Engineering,Zhejiang University,Hangzhou 310027)
出处
《自动化学报》
EI
CSCD
北大核心
2020年第10期2072-2091,共20页
Acta Automatica Sinica
基金
NSFC-浙江省两化融合基金(U1709211)
浙江省重点研发计划项目(2019C03100)
浙江省重点研发计划项目(2019C01048)资助。
关键词
数据解析
智能制造
间歇过程
非平稳
状态监测
故障诊断
机器学习
Data analytics
intelligent manufacturing
batch process
nonstationary
process monitoring
fault diagnosis
machine learning
